Viewing and Manipulating a GNN

Cytoscape Requirements: GNN files are opened and viewed in Cytoscape 3.0 (or
above) using the Organic layout for the Colored SSN and the Prefuse Force
Directed layout for the GNNs.

Opening the colored SSN and both formats of the GNN in a single session of
Cytoscape allows fast comparison between the three networks.

Colored SSN: The xgmml file for the colored SSN is opened, visualized using the
Organic layout, and analyzed like the SSNs generated with EFI-EST.

GNNs: The Prefuse Force Directed layout places the most connected GNN-clusters
at the top of the layout. For the Pfam hub-node format, these are the most
commonly occurring Pfam families. For the SSN query cluster hub-node format,
these are the SSN clusters that identify the largest number of Pfam families.

For the SSN query cluster hub-node format, the most connected GNN clusters
often are those for the input SSN clusters with the fewest number of sequences.
This may seem counterintuitive, but the fewer the number of genomes, the higher
the co-occurrence for a neighbor. Depending on the minimum co-occurrence
frequency selected for displaying Pfam families, those clusters with a small
number of sequences will retain many/most of the neighbors and their Pfam
families. Therefore, neighbors that are present by "random chance" will be
retained, although they are not functionally linked to the query sequences.
Many of these will be in the "noise" and removed when the input cluster has a
large number of sequences.

For clusters with a large number of sequences, a "large" co-occurrence
frequency, e.g., 20%, will eliminate neighbor Pfam families that occur in only
a small fraction of the genomes that contain the query sequences. Thus,
neighbors that occur by "random chance" and are functionally unlinked to the
queries will be excluded from the GNN.

Filtering GNNs: For entire protein families, GNNs generated with a ±10 orf
window and a small co-occurrence frequency, e.g., ≤10%, will include a huge
amount of information. Therefore, it will be useful, even essential, to
filter/simplify the GNNs.

The SSN cluster hub-node format GNN immediately allows the user to focus on the
neighbors for individual input SSN clusters. In contrast to filtering a Pfam
hub-node GNN, a single cluster is present for each SSN cluster, with
spoke-nodes for all of the Pfam families that are identified and satisfy the
user-selected co-occurrence frequency. These GNNs will have the same number of
clusters as the input SSN.

For Pfam family hub-node GNNs generated with a multiple SSN clusters (in the
extreme an entire protein family), the user may want to focus on a single Pfam
family to determine whether the input SSN may be "over-fractionated" so
multiple clusters find the same genome neighbors. Alternatively, it may be
useful to filter this GNN to select one or more specific SSN query spoke-nodes
and their directly connected Pfam family hub-nodes and generate a daughter GNN
with only those hub- and spoke-nodes; this GNN will contain as many clusters as
Pfam families that were identified as neighbors to the SSN cluster.

For SSNs with many clusters, the user can select a specific cluster in the
input SSN for more detailed examination, e.g., the SSN cluster hub-node and
then its Pfam family spoke-nodes can be selected and a daughter network with
the single SSN cluster hub-node can be generated.

The identities of the Pfam families for the neighbors together with their
query-neighbor distances and co-occurrence frequencies often is sufficient to
distinguish functionally linked neighbors (members of pathways) from
functionally unlinked neighbors ("noise").

Figure 1. A full GNN prepared with EFI-GNT for the Radical SAM family
[left] and a GNN that has been filtered for SSN-cluster 93 [right].

Note that the length of the edge that connects the hub- and spoke-nodes has no
significance. Therefore, for crowded GNN-clusters, feel free to click+drag+drop
overlapping spoke-nodes until all are visible.

Figure 2. A crowded GNN-cluster can be manipulated to remove overlapping nodes.